LPG PCA算法在医学图像中的性能分析

R. Hari Kumar, B. Vinoth kumar, S. Gowthami
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引用次数: 4

摘要

本文对LPG主成分分析算法在医学图像中的性能进行了分析。包含大量信息的医学图像经常受到噪声和伪影的影响,导致诊断效率低下。液化石油气主成分分析是一种统计去相关技术,是提高医学图像性能的有效方法之一。为了更好地保存图像中的精细结构,将像素及其最近邻居建模为矢量变量,使用图像中的移动窗口选择其训练样本。这种局部矢量变量保存导致了相似强度特征的选择。该方法分两个阶段进行,以提高去噪性能。使用各种图像质量指标对该技术进行了性能分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Performance analysis of LPG PCA algorithm in medical images
This paper presents the performance analysis of the LPG PCA algorithm in medical images. Medical images containing lot of information are often affected by noise and artifacts, which leads to the inefficient diagnosis. LPG PCA which is a statistical decorrelation technique is found to be one of the efficient methods which could be used in improving the performance of medical images. For better preservation of fine structures in an image, a pixel and its nearest neighbors are modeled as a vector variable whose training samples are selected using a moving window in the image. Such a local vector variable preservation leads to the selection of similar intensity characteristics. This method is done in two stages for improving the denoising performance. Performance analysis of this technique is found using various image quality measures.
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